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Showing results for tags 'large language models'.
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Q 732. If you upload datasets in an Excel file and use an LLM like ChatGPT to analyze whether there is a significant difference between two sets of continuous data, what are the potential pitfalls or errors that could occur during the analysis? Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honourable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error-prone because our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved. -
LLMs and Problem Solving
Vishwadeep Khatri posted a question in We ask and you answer! The best answer wins!
Q 718. What are the kind of problems that today’s LLMs cannot solve? Provide an example of such a problem with evidence. Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved. -
Q 716. Compare RAG with fine-tuning for an LLM-powered Agent. When will you use RAG? When will you use fine-tuning? When would you like to use a combination? Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved.
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Named Entity Recognition (NER)
Vishwadeep Khatri posted a question in We ask and you answer! The best answer wins!
Q 714. How do Named Entity Recognition (NER) systems handle ambiguous terms, and what techniques can enhance their accuracy in real-world applications? Try running this through different large language models (LLMs) and share the varied responses as examples. Feel free to compare their outputs for added insights! Note for website visitors - This platform hosts two weekly questions, one on Tuesday and the other on Friday. All previous questions can be found here: https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/. To participate in the current question, please visit the forum homepage at https://www.benchmarksixsigma.com/forum/. The question will be open until Tuesday or Friday at 5 PM Indian Standard Time, depending on the launch day. Responses will not be visible until they are reviewed, and only non-plagiarised answers with less than 5-10% plagiarism will be approved. If you are unsure about plagiarism, please check your answer using a plagiarism checker tool such as https://smallseotools.com/plagiarism-checker/ before submitting. All correct answers shall be published, and the top-rated answer will be displayed first. The author will receive an honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term. Some people seem to be using AI platforms to find forum answers. This is a risky approach as AI responses are error prone as our questions are application-oriented (they are never straightforward). Have a look at this funny example - https://www.benchmarksixsigma.com/forum/topic/39458-using-ai-to-respond-to-forum-questions/ We also use an AI content detector at https://quillbot.com/ai-content-detector. Only answers with less than 45-50% AI-generated content will be approved.